CRAN/E | VarSelLCM

VarSelLCM

Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values

Installation

About

Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here doi:10.1007/s11222-016-9670-1). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.

Citation VarSelLCM citation info
varsellcm.r-forge.r-project.org/

Key Metrics

Version 2.1.3.1
R ≥ 3.3
Published 2020-10-14 1291 days ago
Needs compilation? yes
License GPL-2
License GPL-3
CRAN checks VarSelLCM results

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Maintainer

Maintainer

Mohammed Sedki

mohammed.sedki@u-psud.fr

Authors

Matthieu Marbac
Mohammed Sedki

Material

NEWS
Reference manual
Package source

In Views

Cluster
MissingData

Vignettes

Vignette VarSelLCM

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

VarSelLCM archive

Depends

R ≥ 3.3

Imports

methods
Rcpp ≥ 0.11.1
parallel
mgcv
ggplot2
shiny

Suggests

knitr
rmarkdown
dplyr
htmltools
scales
plyr

LinkingTo

Rcpp
RcppArmadillo

Reverse Imports

ClusVis

Reverse Suggests

FCPS